MR Image Super Resolution based on Sparse Representation by Using Constrained Optimization

  • N . Arivazhaki, S . Anbhumozhi
  • Published 2016

Abstract

Most natural images can be approximated using their low-rank components. This fact has been successfully exploited in recent advancements of matrix completion algorithms for image recovery. However, a major limitation of low-rank matrix completion algorithms is that they cannot recover the case where a whole row or column is missing. The missing row or column will be simply filled as an arbitrary combination of other rows or columns with known values. This precludes the application of matrix completion to problems such as superresolution (SR) where missing values in many rows and columns need to be recovered in the process of up-sampling a low-resolution image. Enhancement of the images will be more helpful in medical, surveillances and remote sensing applications. While increasing the size of the image the original image quality will be affected.Inorder to avoid the loss of quality while enhancing the image, Low rank optimization based on TV and Non Local Means (NLM) Optimized Sparse Method is used. The noises and the pixel differences occurring in the up sampling and down sampling of the images were identified and they were removed based on the proposed method. The performance of the proposed method is proved using the performance parameters. The main objective of the process is to increase the resolution of the input image. To handle the noises occurring due to the up sampling and down sampling process using optimization methods. To includes Low rank optimization based on TV for the enhancement of the input images.

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Cite this paper

@inproceedings{Arivazhaki2016MRIS, title={MR Image Super Resolution based on Sparse Representation by Using Constrained Optimization}, author={N . Arivazhaki and S . Anbhumozhi}, year={2016} }